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Schlagworte
- Classification (2)
- Multispektralklassifizierung (2)
- Zerstörungsfreie Prüfung im Bauwesen (2)
- 3D laser scanning (1)
- 3D-Mikroskopie (1)
- AFM (1)
- Acquisition (1)
- Bauwerksoberflächen (1)
- CLSM (1)
- Cultural Heritage (1)
Organisationseinheit der BAM
Kalibrierung unterschiedlicher 3D-Messmethoden mit einem vielseitig einsetzbaren Kalibrierobjekt
(2005)
Multi-spectral data acquisition and processing techniques for damage detection on building surfaces
(2006)
ZfPBau-Kompendium 2004
(2007)
Dieses Kompendium ist ein kurzgefasster Abriss, ein Leitfaden und auch ein Handbuch der für den Einsatz im Bauwesen geeigneten zerstörungsfreien Prüfverfahren. In strenger, stets gleicher Gliederung wird jede der mehr als 100 Untersuchungsmethoden in einem eigenen Geräteblatt beschrieben. Hinzu kommen Querverweise zu alternativen Prüfmethoden sowie zahlreiche Hinweise auf einschlägige Fachliteratur und zu bekannt gewordenen Geräteherstellern, Lieferanten und Dienstleistern.
Fractography plays a critical role in failure analysis of engineering components and has a considerable importance for safety investigations. Usually, the interpretation of fracture surfaces is done by experts with the help of literature and experimental data, that requires a lot of experience. The use of deep learning (DL) with neural networks in failure analysis becomes more and more relevant with the rapidly developing possibilities. Especially, the modern network architectures can assist fractographers in determining various fracture features on SEM images of the fracture surfaces. The basis for the best possible evaluation is the understanding of the influence of the input data used for training deep neural networks (DNN). Therefore, this study discusses the influence of the selection of the input data used for the prediction quality of these networks in order to take this into account for future data acquisition. Specimens of various metallic materials were subjected to fatigue cracking experiment under laboratory conditions. The fractured surfaces were then imaged using various modes or detectors (such as SE, BSE and topography) in SEM, and those captured images were used to create a training data set. The relevance of the individual data for the quality of the prediction is determined by a specific combination of the different detector data. For the training, the well-established architecture of a UNet-ResNet34 with a fixed set of hyperparameters is used. It has been found in this present study that the combination of all input data significantly increases the prediction accuracy, whereby even the combination of SE and BSE data provides considerable advantages over the exclusive use of SE images.